Similarly, the result of the second query may give the translators an idea about possible lexical transformation: missing need = 14 (5.035) important missing = 8 (2.930) missing vital = 8 (2.322) lack necessary = 204 (1.982)… essential lack = 86 (0.908)… The concordance for the last pair of descriptors contains the phrase they lack the three essentials, which illustrates the transformation.

the resulting translation may be the following: Children attend schools that are in poor repair and lacking basic essentials our system supports translators in making decisions about indirect translation equivalents in a number of ways: it suggests possible structural and lexical transformations for contextual descriptors; it verifies which translation variants co-occur in the tL corpus; and it illustrates the use of the transformed tL lexical descriptors in actual contexts.


it uses scores cos(v q , v w ) generated for words in similarity classes -the cosine of the angle between the collocation vector for a word and the collocation vector for the query or dictionary translation of the query.

words whose equivalents show similar usage in a comparable corpus receive the highest scores.


The intuition behind the objective evaluation experiment is that the capacity of our tool to find indirect translation equivalents in comparable corpora can be compared with the results of automatic alignment of parallel texts used in translation models in SMT: one of the major advantages of the SMT paradigm is its ability to reuse indirect equivalents found in parallel corpora (equivalents that may never come up in hand-crafted dictionaries).

automatically generated GIZA++ dictionaries with word alignment contain many examples of indirect translation equivalents.


Our system returns both descriptors used in human translation as a pairlack essential (ranked 41 without filtering and 22 with the default semantic filter).

for a 2-word combination of the descriptors only the output of our system matched the human solution, which we counted as one hit for the system and no hits for ORD or GIZA.


Again, the objective evaluation experiment gives only a conservative estimate of ranking, because there may be many more useful indirect solutions further up the list in the output of the system which are legitimate variants of the solutions found in the parallel corpus.

evaluation figures should be interpreted in a comparative rather then an absolute sense.


We also construct a bilanguage using the alignment function g similar to the bilanguage using the alignment function f as shown in Equation 3.

the bilanguage corpus obtained by combining the two alignment functions is B = B f ∪ B g .


However, like all other such proposals, they are formulated on fully specified structures, which makes it hard to integrate them into a generative model, where dependency structures are composed from elementary units of lexicalized information.

little is known about the generative capacity and computational complexity of languages over restricted non-projective dependency structures.


Each occurrence .i#k/ in a component of !.u/ represents either the node u itself (i D 0), or the kth block of some direct dependent ui of u (i ¤ 0), in which case we proceed recursively: The root node of a dependency structure has only one block.

to linearize a tree t , we only need to linearize the first block of the tree's root node: lin.t/ D lin.


In an lcfrs in which all functions are lexicalized, there is a one-to-one correspondence between the nodes in an evaluated tree and the positions in the string that the tree evaluates to.

tree and string implicitly form a dependency structure, and we can speak of the dependency language generated by a lexicalized lcfrs.


In the case of a synchronous almost linear CFLG, the derivation tree found from the source λterm can be used to compute a target λ-term.

to the extent that transduction back and forth between strings and logical forms can be expressed by a synchronous almost linear CFLG, the search problem of finding one logical form of an input sentence and that of finding one surface realization of an input logical form are both in functional LOGCFL.


Thus, to the extent that transduction back and forth between strings and logical forms can be expressed by a synchronous almost linear CFLG, the search problem of finding one logical form of an input sentence and that of finding one surface realization of an input logical form are both in functional LOGCFL.

8 there are efficient parallel algorithms for these problems.


The length of the piece over w's subtree depends on w's position within that subtree, while the other piece does not depend on the internal layout of w's subtree.

the total dependency length for the entire sentence can be divided into: 1. the length of all links within w's subtree plus the length of the first piece of w's link to its parent, i.e.


While the link from w to its parent may point either to the right or left, the optimal layout for w's subtree given that w attaches to its left must be the mirror image of the optimal layout given that w attaches to its right.

only one case need be considered, and the optimal layout for the entire sentence can be computed from the bottom up using just one dynamic programming state for each node in the tree.


Any rearrangement that decreases the length of the link to the left of the head must increase the length of the link to the right of the head by the same amount.

the optimal layout of all i children can be found by placing the two largest children outermost and on opposite sides, the next two largest children next outermost and on op- posite sides, and so on until only one or zero children are left.


The labeler assigns the orchestrate.01 label ARG1 to the same constituent labeled ARG1 in the test sentence, but fails to assign a label to the other argument constituent in the test sentence.

a single mapping is recorded in the ordered list of rolesets, namely that ARG1 of orchestrate.01 can be mapped to ARG1 of instigate.01.


It means that one cooccurrence of the two structures contributes 1 λ to our proposed kernel while it contributes zero to the traditional one.

by this improvement, our method would be able to explore more linguistically appropriate features than the previous one (which is formulated as Improvement 2: Grammar-driven tree nodes approximate matching.


In fact, new problems can be invented for the sole purpose of obtaining a better Θ.

we distinguish between two types of problems in ASO: auxiliary problems, which are used to obtain Θ, and target problems, which are the problems we are aiming to solve 1 .


To have more positive training samples, we only predict the most frequent features.

we will probably predict whether hw=to, but not whether hw=trader, since to occurs more frequently than trader as a head word.


A second objective was to extend the information that could be learned from the process beyond hyponyms of a given word.

the approach was extended to finding lexical patterns that could produce synonyms and other standard lexical relations.


Step 3: For each concept word, group the contexts in which it appears according to the target word that appears in the context.

('X is the capital of Y ' would likely be grouped with 'Y 's capital is X'.)


An example of this is from CCGbank (Hockenmaier, 2003), where all modifiers in noun-noun compound constructions modify the final noun (because the Penn Treebank, from which CCGbank is derived, does not contain the necessary information to obtain the correct bracketing).

there are nonnegligible, systematic errors in both the training and testing material, and the CCG parsers are being rewarded for following particular mistakes.


(1998) describe such a suite, consisting of sentences taken from the Susanne corpus, annotated with Grammatical Relations (GRs) which specify the syntactic relation between a head and dependent.

all that is required to use such a scheme, in theory, is that the parser being evaluated is able to identify heads.


We thus cannot expect the estimated p s (y|x) to work well on the regions where p t (x, y) is high, but p s (x, y) is low.

in this case, we still need domain adaptation, which we refer to as instance adaptation.


To address this, we can sample a state-conditional distribution over child lists λ k from a DP with L k as a base measure.

we augment the basic model given in the previous section with the variables ζ, L k , and λ k : c t |λ k ∼ λ k An important consequence of defining L k locally (instead of globally, using β instead of the π k s) is that the model captures not only what sequences of children a state prefers, but also the individual children that state prefers; if a state gives high probability to some particular sequence of children, then it is likely to also give high probability to other sequences containing those same states, or a subset thereof.


Acquiring labeled data is a difficult and expensive task.

an increasing attention has been recently given to semi-supervised learning, where large amounts of unlabeled data are used to improve the models learned from a small training set (Collins and Singer, 1999;Thelen and Riloff, 2002).


Furthermore, it is unreasonable to implement the exact computation for each constraint.

we implemented a generic approximation for the hamming distance assuming only that we are given a boolean function φ C (y N ) that returns whether labeling the token x N with state y N violates constraint with respect to an already labeled sequence (x 1 , .


That is, if many of the reference system's outputs are similar to those MT outputs that received low assessments, we conclude this reference system may not be of high quality.

if a new translation is found to be similar with this reference system's output, it is more likely for the new translation to also be bad.


In the space of all possible MT outputs, the neighborhood of good translations is much smaller than that of bad translations.

as long as the regression models sees some examples of sentences with high assessment scores during training, it should have a much better estimation of the characteristics of good translations.


The relationship between word alignments and their impact on MT is also investigated in (Ayan and Dorr, 2006;Lopez and Resnik, 2006;Fraser and Marcu, 2006).

we chose to extrinsically evaluate the performance of our approach via the translation task, i.e.


The size of the test set may influence the quality of these alignments.

system outputs from development sets may have to be added to improve the GIZA++ alignments.


There is a concern that the prior probabilities estimated this way may be inaccurate.

the priors may have to be smoothed by a tunable exponent.


However, the optimization experiments showed that the best performance was obtained by having a smoothing factor of 1 which is equivalent to the original priors.

no smoothing was used in the experiments presented later in this paper.


We looked for three constituents c i , c j , c k from a sentence S, such that P pre (c i , c j ) > 0.5, P pre (c j , c k ) > 0.5, P pre (c i , c k ) < 0.5 and found none.

unlike UCHIMOTO, where one needs to make exactly N !


We then indicate how the satisfiability of this enriched input could be controlled.

in the realisation algorithm sketched in Section 3, non-determinism stems from lexical ambiguity: 3 for each (combination of) literal(s) l in the input there usually is more than one TAG elementary tree whose semantics subsumes l. each (combination of) literal(s) in the input selects a set of elementary trees and the realiser output is the set of combinations of selected lexical trees which are licensed by the grammar operations (substitution and adjunction) and whose semantics is the input.


More generally, each elementary tree in the grammar is associated with the set of classes used to produce that tree and importantly, this set of classes (we will call this the tree identifier) provides a distinguishing description (a unique identifier) for that tree: a tree is defined by a specific combination of classes and conversely, a specific combination of classes yields a unique tree.

4 the set of classes associated by the compilation process with a given elementary tree can be used to uniquely identify that tree.


In contrast, NLG geared realisers often have to reconstruct this association in rather ad hoc ways.

for instance, (Yang et al., 1991) resorts to ad hoc "mapping tables" to associate substitution nodes with semantic indices and "fr-nodes" to constrain adjunction to the correct nodes.


Although standard logistic regression optimizes a convex objective, the inclusion of the hidden variable renders our objective non-convex.

convergence to a global minimum is not guaranteed.


Our model does not address the formation of nounphrase clusters, but only the question of whether each pair of noun phrases in the document corefer.

we evaluate only the binary classification phase, and report results in terms of the area under the ROC curve (AUC).


For simplicity, a parameter sweep identifies the interpolation weights that maximize performance on the test set.

it is likely that these results somewhat overestimate the performance of these baseline models.


While we could have kept the same information after the system was done with this discourse segment, we thought that users will benefit from having the correct answer on the screen (recall NM 4 in Figure 1).

the NM was enhanced to display the correct answer after the system is done with each question.


The users in the first condition (F) had the NM enabled in the first problem and disabled in the second problem, while users in the second condition (S) had the opposite.

if the NM has any effect on the user's perception of the system, we should see a decrease in the questionnaire ratings from problem 1 to problem 2 for F users and an increase for S users.


We believe that this is due to the fact that the essay interpretation component was disabled in this experiment.

the instruction did not match the initial essay quality.


Then, the likelihood of the observations given the parameters can be computed as follows Note that the following equation holds: where the right-hand side is actually the expansion .

the likelihood can be simplified as Switching to the notations w j and o k , we have where δ w i,j = 1 ifw i,j ∈ w i and δ w i,j = 0 otherwise, and δ o i,k = 1 ifõ i,k ∈ o i and δ o i,k = 0 otherwise.


Then, at a given number n of top ranked words, the precision of mapping the given object o k to words is defined as # words that o k is correctly mapped to # words that o k is mapped to and the recall is defined as # words that o k is correctly mapped to # words that o k should be mapped to All the counting above is done within the top n rank.

we can get different precision/recall at different ranks.


Note that while U-DOP* is about 2 orders of magnitudes smaller than UML-DOP for the WSJ10, it is almost 3 orders of magnitudes smaller for the first 2 million sentences of the NANC corpus.

even if U-DOP* does not give the highest f-score in table 1, it is more apt to be trained on larger data sets.


Reranking algorithms (Koo and Collins, 2005;Charniak and Johnson, 2005) search the list of best parses output by a generative parser to find a parse of higher quality than the parse selected by the generative parser.

these algorithms in effect assess parse quality using syntactic and lexical features.


In order to minimize visual clutter, for the filter f-score measure we use the maximum recall (MR) baseline rather than the minimum length (ML) baseline, since the former outperforms the latter.

mL is only shown for the average f-score measure.


We then reverse the logic and determine the contribution of each comment in the pricing power of a merchant.

we discover the polarity and strength of each evaluation without the need for human annotation or any other form of linguistic resource.


Observe that each pair of adjacent levels in the model is equivalent to the original model from Figure 1.

the scores of the every label at each node in the graph can be calculated with a straight-forward bottom-up dynamic programming algorithm.


The thick horizontal bars are the accuracies of the in-domain classifiers for these domains.

the first set of bars shows that the baseline achieves 72.8% accuracy adapting from DVDs to books.


In our work we only show improvement for the basic model, but all of these new techniques also make use of lexical features.

we believe that our adaptation methods could be also applied to those more refined models.


Each output template is suggested as holding an entailment relation with the input template I, but the algorithms do not specify the entailment direction(s).

each pair {I, O j } induces two candidate directional entailment rules: 'I → O j ' and 'O j → I'.


For each text to segment, we first identify its topics by performing an unsupervised clustering of its words according to their co-occurrents in the text.

each of its topics is represented by a subset of its vocabulary.


After filtering the low frequency words of the text (frequency < 3), the co-occurrents of the remaining words are classically collected by recording the co-occurrences in a fixed-size window (15 plain words) moved over the pre-processed text.

each text word is represented by a vector that contains its co-occurrents and their cooccurrence frequency.


This evaluation is also a weak point as card(W l ∩ W r ) only relies on word reiteration.

two different words that respectively belongs to W l and W r but also belong to the same text topic cannot contribute to the identification of a possible topical similarity between the two sides of the focus window.


As can be seen from these results, our improved configuration of SPADE gives us large improvements over the previously reported SPADE performance on this subset.

we feel that we can use SPADE as a very strong baseline for evaluation on the entire test set.


Because edus are typically multi-word strings, it is less likely for a word near the beginning or end of a sentence to be at an edu boundary.

it is reasonable to expect the position within a sentence of a token to be a helpful feature.


Whereas most sentences correspond to a well-formed subtree, Sporleder and Lascarides (2004) report that over 20% of the paragraph boundaries in the RST-DT do not correspond to a well-formed subtree in the human annotated discourse parse for that document.

to perform accurate and efficient parsing of the RST-DT at the paragraph level, the text should be segmented into paragraph-like segments that conform to the human-annotated subtree boundaries, just as sentences are segmented into edus.


However, this presents several challenges: (1) The findings result from studies of genres of language, such as stream-ofconsciousness essays (Pennebaker and King, 1999), and informal conversations (Mehl et al., 2006), and thus may not apply to fixed content domains used in NLG; (2) Most findings are based on self-reports of personality, but we want to affect observer's perceptions; (3) The findings consist of weak but significant correlations, so that individual parameters may not have a strong enough effect to produce recognizable variation within a single utterance; (4) There are many possible mappings of the findings to generation parameters; and (5) It is unclear whether only specific speech-act types manifest personality or whether all utterances do.


The probability of the operations biases the production of complex clauses, periods and formal cue words for introverts, to express their preference for complex syn-tactic constructions, long pauses and rich vocabulary (Furnham, 1990).

the introvert parameters favor operations such as RELATIVE CLAUSE for the INFER relation, PERIOD HOWEVER CUE WORD for CONTRAST, and ALTHOUGH ADVERBIAL CLAUSE for CONCESS, that we hypothesize to result in more formal language.


Hedges: Hedges correlate with introversion (Pennebaker and King, 1999) and affect politeness (Brown and Levinson, 1987).

there are parameters for inserting a wide range of hedges, both affective and epistemic, such as kind of, sort of, quite, rather, somewhat, like, around, err, I think that, it seems that, it seems to me that, and I mean.


These acoustic patterns correspond to high-frequency words and phrases, but they only cover a fraction of the words that appear in the input.

the distributional profile obtained during this process is too sparse to deliver robust topic analysis.


Continuous speech includes many word sequences that lack clear low-level acoustic cues to denote word boundaries.

we cannot perform this task through simple counting of speech segments separated by silence.


This alignment procedure may produce paths with high distortion subpaths.

we trim each path to retain the subpath with lowest average distortion and length at least L. More formally, given an alignment of length N , we seek to find m and n such that: We accomplish this by computing the length constrained minimum average distortion subsequence of the path sequence using an O(N log(L)) algorithm proposed by Lin et al (2002).


When applied to a comparison matrix, anisotropic diffusion reduces score variability within homogeneous re-gions of the matrix and makes edges between these regions more pronounced.

this transformation facilitates boundary detection, potentially increasing segmentation accuracy.


Their model assumes that a definite NP and its hypernym words usually co-occur in texts.

for a definite-NP anaphor, a preceding NP that has a high co-occurrence statistics in a large corpus is preferred for the antecedent.


In our study, we use pointwise mutual information (Cover and Thomas, 1991) to measure association strength, which has been proved effective in the task of semantic relation identification (Pantel and Pennacchiotti, 2006).

under pointwise mutual information (PMI), the strength of association between two events x and y is defined as follows: the association between a pattern p and a positive seed pair s: where |(E i ,p,E j )| is the count of strings matched by pattern p instantiated with E i and E j .


is found in the table "list of empires".

the pair "American:United States", which is deemed coreferential in ACE, can be identified by the pattern.


Our model learns title generation in a fully discriminative framework, in contrast to the commonly used noisy-channel model.

instead of independently modeling the selection and grammaticality constraints, we learn both types of features in a single framework.


As with the local model (step one), the number of possible tables-of-contents is too large to be considered exhaustively.

we incrementally construct a table-of-contents by traversing the tree of segments in a pre-order walk (i.e., the order in which segments appear in the text).


an undirected link between s i and s j (iKj) is constructed and the associated weight is their content similarity.

we construct an undirected graph G SS to reflect the homogeneous relationship between sentences.


For instance, beer glass translates into tarro de cerveza in Spanish, bicchiere da birra in Italian, verreà bière in French, and pahar de bere in Romanian.

an important research question is how do the syntactic constructions in the target language contribute to the preservation of meaning in context.


the corresponding ergative nominalization the disbandment of the group encodes a THEME relation and not AGENT.


Since our intuition is that the more translations are provided for an English noun phrase instance, the better the results, we wanted to see what is the impact of each Romance language on the overall performance.

sV M 2 shows the results obtained for English and the Romance language that contributed the least to the performance (F1-F12).


Here we computed the performance on all five English -Romance language combinations and chose the Romance language that provided the best result.

sVM #2, #3, #4, #5, and #6 add spanish, French, Italian, Portuguese, and Romanian in this order and show the contribution of each Romance preposition and all features for English.


The many-to-many mappings of the prepositions (especially of/de) to the semantic classes adds to the complexity of the interpretation task.

for the interpretation of these constructions a system must rely on the semantic information of the preposition and two constituent nouns in particular, and on context in general.


This shows that for more semantically ambiguous noun -noun pairs, the webbased probabilistic model introduces a significant number of false positives.

the more abstract the categories, the more noun compounds are covered, but also the more room for variation as to which category a compound should be assigned.


This approach can sometimes be misled by textual features correlated with the specific entities in the few training pairs provided.

we also describe a method for weighting features in order to focus on those correlated with the target relation rather than with the individual entities.


We propose a rule representation that supports this strategy.

our learning approach is seed-driven and bottom-up.


We consider only trees or their subtrees containing seed arguments.

our method is much more efficient than the subtree model of Sudo et al., (2003), where all subtrees containing verbs are taken into account.


The distances between events are shortened to a few steps.

we can reach most events in a few iterations.


First, Taboada and Mann (2005) reported that discourse analysis helps to decompose long sentences into clauses.

it helps to distinguish relevant clauses from non-relevant ones.


All the discourse relation classes are potentially useful, since they encode some knowledge about textual structure.

we decide to include all of them in the learning process to learn patterns with best possible performance.


We also notice that multiple instances require modeling of entities in the path.

in our future work we need to enrich the search space for relation patterns.


Another advantage of our approach is in using domain-independent parsers and features.

aRE may be easily portable into new domains.


The baseline SRES does not utilize a named entity recognizer, instead using a shallow parser for exracting the relation slots.

the only allowed entity types are ProperNP, CommonNP, and AnyNP, which mean the heads of, respectively, proper, common, and arbitrary noun phrases.


But since the entity (one of the parts of the candidate) has a high frequency, there is a chance that the relation instance, in which the entity appears, will be repeated elsewhere in the corpus and will be extracted correctly there.

in such case, the simplest recourse is to simply label the entity as Invalid, and not to try fixing the boundaries.


The Person entities have much more rigid internal structure than the other entities -Companies and Inventions.

the best performing of  the three validators is the rule-based, which directly tests this internal structure.


In contrast to global constraints, properties of individual non-projective edges allow us to pinpoint the causes of non-projectivity.

they provide a means for a much more fine-grained classification of non-projective structures occurring in natural language.


No improvement over the baseline was achieved even with this oracle.

the problem with their protocol does not lie with the parse quality assessment function; no other function would produce results better than the oracle.


Only visible variables can result in information being reflected backward through a directed edge, so it is impossible for anything in the unknown portion of the graph to affect the probabilities in the known portion of the graph.

inference can be performed by simply ignoring the unknown portion of the graph, and there is no need to sum over all possible structures for the unknown portion of the graph, as would be necessary for an undirected graphical model.


The resulting estimate of the tree probability is given by: This approximation method replicates exactly the computation of the feed-forward neural network in (Henderson, 2003), where the above means µ t i are equivalent to the neural network hidden unit activations.

that neural network probability model can be regarded as a simple approximation to the graphical model introduced in section 3.1.


It is able to correctly align the last two characters of the string, before generating repeated null alignments.

the central region -substrings of the source and target which remained unaligned plus one extra aligned segment to the left and right -is entered as a new pair to the system (ici, ), as shown in the line labelled Input 2 in the figure.


However, this is not the case for vowel sequences which incorporate a level of knowledge about any segment neighbours.

for the example "shelley", the first segment is "sh" which belongs to C pattern.


When the initial transliteration happens from English to Persian, the transliterator (whether human or machine) uses the rules of transliterating from English as the source language.

transliterating back to the original language should consider the original process, to avoid losing essential information.


the French/English recettes:proceeds) that we would not actually deem to be cognate.

the errors that our system makes are often either linguistically interesting or point out mistakes in our automatically-labelled bitext and (to a lesser extent) dictionary data.


Note that most of the subtasks conduct 'cleaning' of noises, except paragraph boundary detection and sentence boundary detection.

of text normalization, a text is segmented into paragraphs; each paragraph is segmented into sentences with clear boundaries; and each word is converted into the canonical form.


Because REALM pre-computes language models based on its corpus and does not require any hand-tagged seeds, it is far more scalable than approaches that learn models for each individual relation from handtagged data.

rEALM is ideally suited for open information extraction where the relations of interest are not specified in advance and their number is potentially vast.


1 E R is generated by applying an extraction mechanism, typically a set of extraction "patterns", to each sentence in a corpus, and recording the results.

many elements of E R are identical extractions derived from different sentences in the corpus.


This task definition is notable for the minimal inputs required-IE assessment does not require knowing the relation name nor does it require handtagged seed examples of the relation.

an IE Assessor is applicable to Open IE.


In contrast, in the HMM, the non-sparse tokens Ohio and Illinois are likely to have similar state distributions, as they are both the names of U.S. States.

in the state space employed by the HMM, the contexts in phrases (i) and (ii) are in fact quite similar, allowing HMM-T to detect that Pickerington and Chicago are likely of the same type.


The time required to learn HMM-T's parameters scales proportional to T k+1 times the corpus size.

for tractability, HMM-T uses a relatively small state space of T = 20 states and a limited k value of 3.


Further, by considering only consecutive argument pairs, the number of distinct argument pairs in the model grows at most linearly with the number of sentences in the corpus.


In essence, pattern-learning methods run in time linear in the number of relations whereas REALM's run time is constant in the number of relations.

rEALM scales readily to large numbers of relations whereas pattern-learning methods do not.


This usually results in very complex auxiliary rules, especially on real-world training data, making both training and decoding very slow.

we construct auxiliary rules in decoding instead.


However, these models are often fully lexicalized and sensitive to individual phrases.


However, since it is difficult to specify a well-defined rule, Hiero has to rely on weak heuristics (i.e., length-based thresholds) to extract rules.

hiero produces grammars of enormous size.


Consider the sentence pair in NIST MT-2005 test set shown in figure 1(a): after translating the word " /mend", the decoder should 'jump' across six words and translate the last phrase " /fissures in the relationship".

while short-distance reordering is under the scope of the distance-based model, long-distance reordering is simply out of the question.


Starting from the leaves of t S , for each node N covering phrase p, we only keep track of the n p s that have the highest reordering probability.

r(N ) ≤ n. There are at most 2n 2 reorderings for any node and only the top-scored n reorderings are recorded.


Pharaoh's lexical weights require access to word-alignments; calculating these alignments between the source and target words in a phrase would prove difficult for a triangulated model.

we use a modified lexical score, corresponding to the maximum IBM model 1 score for the phrase pair: where the maximisation 4 ranges over all one-tomany alignments and Z normalises the score by the number of possible alignments.


For L = 2 n the computed approximate expected Fscores were indistinguishable from their exact values.

the approximate decoder found the true maximum every time.


In this case, an elementary calculation shows that the MAP estimate isθ = n H /10, where n H is the number of heads in w (likewise, n T is the number of tails).

p (t|w,θ) favors t = 1 for any sequence that does not contain exactly five heads, and assigns equal probability to t = 1 and t = 0 for any sequence that does contain exactly five heads -a counterintuitive result.


Typically, linguistic structures are characterized by sparse distributions (e.g., POS tags are followed with high probability by only a few other tags, and have highly skewed output distributions).

it makes sense to use a Dirichlet prior with β < 1.


For a span p and a state s, we denote the associated top hypothesis as where V is the score of a hypothesis (defined in (1) below).

similarly, we denote the top state for p as for each span p, we have a top hypothesis p.s.T , whose score is the highest among all the hypotheses for span p. Hypotheses are started and grown by means of labeling actions.


In our algorithm, the gold standard compatible hypotheses are used for weight update only.

after each sentence is processed, the weight vector can usually successfully predict the gold standard parse.


As a result, after each sentence is processed, the weight vector can usually successfully predict the gold standard parse.

our learning algorithm is aggressive on weight update.


However, evaluation methodologies must evolve to reflect the shifting interests of the research community to remain relevant.

organizers of evaluations must walk a fine line between progress and chaos.


For the latter, step 0 assigns ∆(c j r , c j r ) = 0.

∆(r, r) = nc(r) j=1 (1 + 0) − 1 = 0 and the base case holds.


We note that Google is outperformed by YourQA since its ranks are based on whole documents, not on single passages.

google may rank a document containing several sparsely distributed question words higher than documents with several words concentrated in one passage, which are more interesting.


Gazetteers: There are few available gazetteers for Chinese and Japanese.

we extracted location data from language-specific resources.


Thus we define the strength of priming as the decay rate of repetition probability, from shortly after the prime to 15 seconds afterward (predictor: DIST).

we take several samples at varying distances (d), looking at cases of structural repetition, and cases where structure has not been repeated.


Priming of a syntactic construction shows up in the tendency to repeat such rules in different lexical contexts.

we examine whether syntactic rules have been repeated at a distance d. For each syntactic rule that occurs at time t 1 , we check a one-second time pe- for an occurrence of the same rule, which would constitute a prime.


Thus, we examine whether syntactic rules have been repeated at a distance d. For each syntactic rule that occurs at time t 1 , we check a one-second time pe- for an occurrence of the same rule, which would constitute a prime.

the model will be able to implicitly estimate the probability of repetition.


This metric requires all annotations to contain the same set of markables, a condition that is not met in our case.

we report α values computed on the intersection of the com-pared annotations, i.e.


Thus, so long as each coordinate of any two consecutive search points does not pass through zero, ( ) does not contribute at all to the curvature of the function on the segment joining them.

we can use L-BFGS to approximate the Hessian of alone, and use it to build an approximation to the full regularized objective that is valid on a given orthant.


First, an assumption in our learning model is that the rules corresponding to the grammar preterminals are given.

for a given set of representative examples, f , we can build the grammar § using a bottom-up robust parser, which returns partial analyses (chunks) if it cannot return a full parse.


For all syntagmas we can say in polynomial time whether they belong or not to the grammar language.

using the~} as generalization model, our grammar induction problem is decidable.


The reader should remember that for a LWFG , there exists a partial ordering among the grammar nonterminals, which allows a total ordering of the representative examples of the grammar .

in this algorithm, the learner has access to the ordered representative syntagmas when learning the grammar.


Unlike the character-tagging models, the CRF submodel assigns tags to subwords, which include single-character words and the most frequent multiple-character words from the training corpus.

it can be seen as a step towards a word-based model.


However, most draws will have weights which decay exponentially quickly in the prior (though not necessarily in the posterior).

there is a natural penalty for each cluster which is actually used.


Then, we build an interpolated model by performing linear interpolation on the standard model and the pivot model.

the interpolated model can employ both the small L f -L e corpus and the large L f -L p and L p -L e corpora.


It strengthens the frequently aligned pairs and weakens the infrequently aligned pairs.

the following sections will use the phrase method to estimate the lexical weight.


For example, the English [p] sound does not exist in Arabic, and the Arabic [É] sound (made by the letter ) does not exist in English.

paul is transliterated as Ã º [bul], and ª [Éali] is transliterated as Ali.


The transducer approach, however, requires a large set of training examples, which is a limitation not present in the fuzzy matching algorithm.

we propose a bootstrapping approach (Yarowsky, 1995) to train the stochastic transducer iteratively as it extracts transliterations from a bitext.


The confidence of the classifier's prediction can then be interpreted as a judgment on the translation quality of the sentence.

the composite metric is encoded in the confidence scores of the classification labels.


The 2004 dataset has many more participating systems, and they span a wider range of qualities.

it is easier to achieve a high rank correlation on this dataset than previous years because most metrics can qualitatively discern that sentences from one MT system are better than those from another.


In fact, as done in standard information retrieval research, our aim is to determine for each ranking the precision/recall trade-off when considering more or less of the items starting from the top of the ranked list.

we evaluate our approach calculating precision at standard recall levels as typically done in information retrieval research (compare (Baeza-Yates and Ribeiro-Neto, 1999)).


we calculate the F-Measure for all the items in the (unranked) list of qualia elements.

for the rankings to be useful, they need to yield higher F-Measures than this naive baseline.


Thus, in the BNC document mentioned above, the word chondritic is counted only once, whereas the other three occurrences are considered as tokens of the echo type.

we are counting what in the information retrieval literature is known as document frequencies.


Thus, no type present in the original corpus disappears (more precisely, V increases by 1 because of the addition of the echo type, but given the large size of V this can be ignored for all practical purposes).

the expected V computed for a specified sample size N with a model trained on an echo-adjusted corpus can be directly compared to observed values at N , and to predictions made for the same N by models trained on an unprocessed corpus.


For any reasonably sized corpus, it is unlikely that function words and common content words will occur in less than 16 documents, and thus their distribution will be irrelevant for model fitting.

we can ignore the issue of what is the boundary between topical words to be echo-adjusted and general words, as long as we can be confident that the set of lowest frequency words used for model fitting belong to the topical set.


Both phenomena come from the deletion of synchronization points.

a projection which deletes only symbols from Σ is safe.


Traditional explanations for the gaps, such as homophony avoidance (Švedova 1982) are also unsatisfactory since they can, at best, explain only a small percentage of the gaps.

the data suggest that gaps persist in Russian primarily because they are not uttered, and this non-use is learned by succeeding generations of Russian speakers.


This assumption states that if the optimal path through a graph happens to go through state q, then this optimal path must include the best path up to and including q.

once an optimal path to state q is found, all other paths to q can be eliminated from the search.


As mentioned earlier, due to the lexicon projection process that is performed via a bilingual dictionary, the entries in our Romanian subjectivity lexicon are in a lemmatized form.

we also lemmatize the gold-standard corpus, to allow for the identification of matches with the lexicon.


Generating seeds for nspec is much more difficult, as integrity requires the absence of hedge cues, and this cannot be done automatically.

we used the following procedure to obtain a set of nspec seeds: 1.


ment boundaries, are often used for non-discourse purposes, such as making a semantic contribution to an utterance.

6 we hypothesize that these ambiguous cue words have led the LX1 model to overpredict.


Individuals need to be able to retrieve the relevant consultation documents efficiently and effectively.

this work presents a novel mechanism to automatically retrieve the relevant consultation documents with respect to users' problems.


The main characteristic of a symptom chain is that it retains the cause-effect or temporal order of the symptoms within it.

the order of the symptoms must be considered when calculating the similarity of two symptom chains.


First, a negative life event and a symptom can each be expressed by different words with the same or similar meaning.

the word-based models often failed to retrieve the relevant documents when different words were used in the input query.


At the same time, GFLO maintains an extensive database with textual descriptions of professions, called BERUFEnet.

2 we cast the problem of ECG as an IR task, trying to remove the disadvantages of conventional ECG outlined above by letting the user describe her interests in a short natural language essay, called a professional profile.


oven, cakes in the profile and pastries, baker, or confectioner in the document.

we propose to utilize semantic relatedness as a ranking function instead of conventional IR techniques, as will be substantiated below.


As ESA uses all textual information in Wikipedia, the measure shows excellent coverage.

we select it as the second measure for integration into our IR system.


We use a general German stop 6 Values for human performance are for one subject.

they only indicate the approximate difficulty of the task.


If all content words are extracted to build a query, a lot of noise is introduced.

we conducted further experiments with (QT-2) and (QT-3): building the query using only nouns, and using manually assigned keywords based on the tagset of 41 keywords.


This suggests that in order to achieve high IR performance for the task of Electronic Career Guidance, it is necessary to preprocess the topics by performing information extraction to remove the noise from free text essays.

of the preprocessing, natural language essays should be mapped to a set of keywords relevant for describing a person's interests.


prepositions and determiners) but not function words such as verbs or nouns.

unlike most RE systems, O-CRF does not try to recognize semantic classes of entities.


Every verb has as many valency frames as it has meanings (t-manual, page 105).

the query language has to be able to distinguish valency frames and search for each one of them, at least as long as the valency frames differ in their members and not only in their index.


The order of nodes is controlled by attribute deepord, which contains a non-negative real (usually natural) number that sets the order of the nodes from left to right.

we will again need to refer to a value of an attribute of another node but this time with relation other than "equal to".


3: text adjunct "called" of "invention" dropped on aligned hyp word "invented" Scores: Runs D and F: The words in the hypothesis are all found, with the approximately all the exact same syntactic dependencies, also in the text.

i concluded that the given hypothesis: The Zoopraxiscope was invented by Muybridge.


Fox (2002) demonstrated and counted cases where cohesion was not maintained in handaligned sentence-pairs, while Cherry and Lin (2006) showed that a soft cohesion constraint is superior to a hard constraint for word alignment.

we propose a soft version of our cohesion constraint.


Our cohesion constraint directly targets sentences for which an unmodified phrasal decoder produces uncohesive output according to the definition in Section 2.

we present our results not only on each test set in its entirety, but also on the subsets defined by whether or not the baseline naturally produces a cohesive translation.


Numerals and indefinite articles are directly followed by countable nouns to denote the quantity of objects.

in the English-to-Chinese machine translation task we need to take additional efforts to generate the missing measure words in Chinese.


The link between p s and p t can be inferred from SMT decoding result.

the chance of capturing the best measure word increases with the aid of words located in the source window.


If {NULL} is selected, it means that we need not generate any measure word at the current position.

no matter what kinds of measure words they are, we can handle the issue of measure word generation in a unified framework.


Due to noises in the Chinese translations introduced by the SMT system, we cannot correctly identify all the positions to generate measure words.

besides precision we examine recall in our experiments.


The structured space of a synchronous grammar is a natural fit for phrase pair probability estimation, though the search space can be prohibitively large.

we explore efficient algorithms for pruning this space that lead to empirically effective results.


C is our unique pre-terminal for generating terminal multi-word pairs: We parameterize our probabilistic model in the manner of a PCFG: we associate a multinomial distribution with each nonterminal, where each outcome in this distribution corresponds to an expansion of that nonterminal.

specifically, we place one multinomial distribution θ X over the three expansions of the nonterminal X, and another multinomial distribution θ C over the expansions of C. the parameters in our model can be listed as where P is for the inverted rule, P [] for the straight rule, P C for the third rule, satisfying P +P [] +P C = 1, and where e/f P (e/f ) = 1 is a multinomial distribution over phrase pairs.


As j is incremented, we add P (t a |s j ) to I(a).

we have linear time updates for O and I.


For instance, the word "einundzwanzig" (twenty-one) is transcribed as "ein und zwanzig", "Kriegspläne" (war plans) as "Kriegs Pläne" and "BMW" as "B. M. W." These transcription variants are considered to be correct by our evaluation scheme.


Such multi-word lexemes have to be entered into the lexicon, but they cannot directly be identified in the word list.

they have to be extracted from supplementary resources.


Proper nouns considerably contribute to ambiguity, as most of them do not require a determiner.

a proper noun which is a homograph of an open-class word was entered only if it is "relevant" for our task.


Note that the decision rule (1) is over whole documents.

we processes complete documents at a time without prior segmentation into sentences.


A more appropriate loss function is the number of source words that are replaced incorrectly.

we use the following minimum word risk (MWR) decision strategy, which minimizes source word loss.


In such models, the meaning of a word is defined by its relationship to representations of the language users' environment.

for a robot operating in a laboratory setting, words for colors and shapes may be grounded in the outputs of its computer vision system (Roy & Pentland, 2002); while for a simulated agent operating in a virtual world, words for actions and events may be mapped to representations of the agent's plans or goals (Fleischman & Roy, 2005).


Recent work in video surveillance has demonstrated the benefit of representing complex events as temporal relations between lower level subevents (Hongen et al., 2004).

to represent events in the sports domain, we would ideally first represent the basic sub events that occur in sports video (e.g., hitting, throwing, catching, running, etc.)


The styles that different directors employ are extremely consistent within a sport and make up a "language of film" which the machine can take advantage of in order to represent the events taking place in the video.

even though it is not easy to automatically identify a player hitting a ball in video, it is easy to detect features that correlate with hitting, e.g., when a scene focusing on the pitching mound immediately jumps to one zooming in on the field (see Figure 1).


Further, while visual information may be useful for distinguishing the latter error, it is unlikely to assist with the former.

in the next section we examine an extrinsic evaluation in which grounded language models are judged not directly on their effect on word accuracy or error rate, but based on their ability to support video information retrieval.


And, words are entrenched in the writing systems of many languages.

it is generally accepted that children learning their first language must learn how to segment speech into a sequence of words.


Syntactic and communicative constraints make pauses more likely at certain positions than others.

the "supervised" algorithms for this task train on a representative set of word boundaries whereas "unsupervised" algorithms train on a biased set of word boundaries.


So (correctly) no boundary will be marked.

we can compute P (b|r, l) from P (b|r), P (b|l), and P (b).


Moreover, initially, we don't know P (b).

wordEnds bootstraps the estimation using a binary model of the relationship between word and phrase boundaries.


The more common words the definitions of two terms have, the more similar these terms are (Banerjee and Pedersen, 2005).

we can compute the term semantic similarity based on synset definitions in the following way: where D(t) is the concatenation of the definitions for all the synsets containing term t and |D| is the number of words of the set D. Within a taxonomy, synsets are organized by their lexical relations.


Thus, we can compute the term semantic similarity based on synset definitions in the following way: where D(t) is the concatenation of the definitions for all the synsets containing term t and |D| is the number of words of the set D. Within a taxonomy, synsets are organized by their lexical relations.

given a term, related terms can be found in the synsets related to the synsets containing the term.


In practice, the required stemming may vary from one text collection to another.

attempts have been made to use corpus analysis to improve existing rule-based stemmers.


Directly calculating P(e ij ) by summing the probabilities of all paths passing through e ij is an NP problem (Rabiner, 1989), and is intractable if the query is long.

we use the forwardbackward algorithm (Bishop, 2006) to calculate P(e ij ) in a more efficient way.


In our experiments, we only use three features.

the optimal weights can be calculated efficiently even we have a large number of training instances.


If a query contains less than 3 terms or the term under consideration is the beginning/ending term in the query, we will set the probability of the missed term/terms to be 1.

it becomes point-wise mutual information when the query contains only two terms.


However, with the Similarity or Bigram Expansion models, the selection value, either similarity or query likelihood, cannot be compared across the query terms.

more alterations need to be selected, leading to heavier query traffic.


In the step (a), we employ the MDL-based (Minimum Description Length) tree cut model for automatically identifying question topic and question focus.

this section will begin with a brief review of the MDL-based tree cut model and then follow that by an explanation of steps (a) and (b).


According to the definition of a topic chain, the topic terms in a topic chain of a question are ordered by their specificity values.

a cut of a topic chain naturally separates the topic terms of low specificity (representing question focus) from the topic terms of high specificity (representing question topic).


Though we only utilize data from communitybased question answering service in our experiments, we could also use categorized questions from forum sites and FAQ sites.

as future work, we will try to investigate the use of the proposed approach for other kinds of web services.


The ratings differ significantly for all traits but conscientiousness (p ≤ .001).

parameter estimation models can be used in applications that only require discrete binary variation.


When the beam search is complete we have a list of translations in the top beam cell spanning the entire source sentence along with their approximated inside derivation scores.

we can assign each translation string a probability by normalising its inside score by the sum of the inside scores of all the translations spanning the entire sentence.


The space of translation sub-strings is exponential in each cell's span, and our algorithm can only sum over a small fraction of the possible strings.

the resulting probabilities are only estimates.


In this paper, we focus on binary SCFGs and without loss of generality assume that only the preterminal unary rules can generate terminal string pairs.

we are focusing on Inversion Transduction Grammars (Wu, 1997) which are an important subclass of SCFG.


For a given nominal pair (w 1 , w 2 ) and cluster C with n core patterns P core and m unconfirmed patterns P unconf , In this formula, 'appears in' means that the nominal pair appears in instances of this pattern extracted from the original corpus or retrieved from the web at the previous stage.

if some pair appears in most of the patterns of some cluster it receives a high HITS value for this cluster.


In effect, this is what model (6) achieves.

of the assumption of symmetry, both these models are 'bag of words' models and word order insensitive.


In many NER problems, features are often constructed as a series of transformations of the input training data, performed in sequence.

if our task is to identify tokens as either being (O)utside or (I)nside person names, and we are given the labeled sample training sentence: (1) one such useful feature might be: Is the token one slot to the left of the current token Professor?


Specifically, we have shown that hierarchical priors allow the user enough flexibility to customize their semantics to a specific problem, while providing enough structure to resist unintended negative effects when used inappropriately.

hierarchical priors seem a natural, effective and robust choice for transferring learning across NER datasets and tasks.


In the LDA framework, each word is generated from a language model that is indexed by the word's topic assignment.

rather than identifying a single topic for a document, LDA identifies a distribution over topics.


Here we follow (Medlock and Briscoe, 2007) and treat the identification of speculative language as the classification of sentences for either speculative or non-speculative assertions, and extend their methodology in several ways.

given labeled sets S spec and S nspec the task is to train a model that, for each sentence s, is capable of deciding whether a previously unseen s is speculative or not.


Also we included ngram features to encode the lexical context in which a given morpheme appears.

we might have something like: for some words (morphemes) w 1 , w 2 , and w 3 , f w 1 •w 2 (w 3 ) = 1 if w 3 is preceded by w 1 , w 2 ; otherwise, 0.


Though it may be reasonable to expect a user to provide a rating for each aspect, it is unlikely that a user will annotate every sentence and phrase in a review as being relevant to some aspect.

it can be argued that the most pressing challenge in an aspect-based summarization system is to extract all relevant mentions for each aspect, as illustrated in figure 2.


We then make the assumption that the text of the review discussing an aspect is predictive of its rating.

if we model the prediction of aspect ratings jointly with the construction of explicitly associated topics, then such a model should benefit from both higher quality topics and a direct assignment from topics to aspects.


In such cases, non-aspect 'background' words will appear to be the most predictive.

the use of the aspect sentiment classifiers based only on the words assigned to the corresponding topics is problematic.


Intervening non-conjunct nodes are given the same category with the conj feature, resulting in a derivation that can be parsed with the standard CCGbank binary coordination rules: The derivation in Figure 1(b) is produced by these corrections to coordination derivations.

applications of the non-CCG rule shown in (3) have been reduced from 1378 to 145 cases.


It may seem trivial to process in this case, but consider a similarly structured NP: lower court ruling that the U.S. can bar the use of... Our minimalist approach avoids reanalysing the many linguistic constructions that can be dominated by NPs, as this would reinvent the creation of CCGbank.

we only flatten those constituents that partially overlap the NML or JJP bracket.


Our second major contribution is the introduction of novel NER features, a source of semantic information previously unused in parsing.

of this work, internal NP structure is now recoverable by the C&C parser, a result demonstrated by our total performance increase of 1.51% F-score.


Note that we simplify the whole sentence without respect to a particular verb.

this process only needs to be done once per sentence (not once per verb).


This formula is positive when the expected value of the l th feature is higher on the set of correct simple labelings K sv than on the set of all simple labelings C sv .

the optimization procedure will tend to be self-reinforcing, increasing the score of correct simple labelings which already have a high score.


We evaluated our system using the setup of the Conll 2005 semantic role labeling task.

2 we trained on Sections 2-21 of PropBank and used Section 24 as development data.


Note that the baseline feature set includes a feature corresponding to the subcategorization of the verb (specifically, the sequence of nonterminals which are children of the predicate's parent node).

baseline does have access to something similar to our model's role pattern feature, although the baseline subcategorization feature only includes post-verbal modifiers and is generally much noisier because it operates on the original sentence.


Any constituents that are not present are then assigned a probability distribution over possible roles according to Baseline.

we fall back Baseline whenever the current simple sentence does not have an "opinion" about the role of a particular constituent.


Our method differs from past work in that it extracts its role pattern feature from the simplified sentence.

the feature is less noisy 3 http://www2.parc.com/isl/groups/nltt/xle/ and generalizes better across syntactic variation than a feature extracted from the original sentence.


The score is designed as follows: for each sentence s, one essential selection has a score of 3, one optional selection has a score of 1.

the GSValue of a sentence ranges from 0 to 15 (5 human summarizers x 3).


This is a unique object for which we are able to define a proper probability model.

our proposed model is a proper model assigning probability mass to all π, L pairs, where π is a parse tree and L is the one and only lattice that a sequence of characters (and spaces) W over our alpha-beth gives rise to.


Since all of the features are binary, a coefficient of β indicates that the corresponding feature, when present, adds a weight of β to the log odds (i.e., multiplies the odds of an error by a factor of e β ).

features with positive coefficients increase the odds of an error, and features with negative coefficients decrease the odds of an error.


However, the clustering algorithms, especially the EM-based algorithms, are computationally expensive.

performing the clustering with a vocabulary that is large enough to cover the many named entities required to improve the accuracy of NER is difficult.


However, Z cannot be calculated without knowing the results of other cluster nodes.

if we use MPI for parallelization, the parallelized version of this routine should resemble the algorithm shown in Figure 1.


Although the above is the essence of our parallelization, invoking MPI Allreduce in the inner-most loop is very expensive because the communication setup is not so cheap.

our implementation calculates p(c l |vt i , n i ) in batches of B examples and calls MPI Allreduce at every B examples.


This is because the entities are extracted from the English monolingual corpora, which has a much larger vocabulary than the English side of the parallel corpora.

we should remove all the Chinese translations that contain any untranslated English words before proceeding to the next step.


Many scholars hypothesize that the behavior of a verb, particularly with respect to the expression of arguments and the assignment of semantic roles is to a large extent driven by deep semantic regularities (Dowty, 1991;Green, 1974;Goldberg, 1995;Levin, 1993).

measurements of verb frame patterns can perhaps be used to probe for linguistically relevant aspects of verb meanings.


It would be ideal if a requester could rely on the voluntary workforce alone for results, but in many cases this may result either in too few workers and/or too slow a rate of data acquisition.

it is often necessary to raise the reward and rely on efficient automatic validation of the data.


In addition to the lexical part, for each extracted feature, we keep a direction.

the preposition of represents two different features in the following examples "member of the committee" and "committee of members".


Moreover, different applications produce different types of challenges and different opportunities.

optimizing a single recognition system for all transcription tasks is well beyond the reach of present technology, and even systems that are apparently similar on average can make different mistakes on different sources.


one algorithm uses the top three attributes to produce clusters, defined by either a single value (e.g., UM-Sing) or by the set of values that comprise a significant portion of the total (e.g.,  Figure 3: A partial tree for Indian restaurants in London, using price range as the predictor variable and food quality as the dependent variable.


With two attributes, the average cluster size at the leaf node is 60.4, but drops to 4.2 with three attributes.

we use two attributes to produce associative clusters, as shown in Fig.


In a final experiment, for each task, we use the highest rated summary as a class to be predicted using C4.5 (Quinlan, 1993).

we have 4 classes: Ref-Sing, Ref-Assoc, UM-Sing, and UM-Assoc.


the parameters can be directly estimated from the training data with relative frequencies.


We should note that this method does not represent standard n-best reranking because the input from the base MT system contains sequences of stems, and the model is generating fully inflected translations from them.

the chosen translation may not be in the provided nbest list.


In addition, using a trigram LM on stems may lead to larger violations of the Markov independence assumptions, than using a trigram LM on fully inflected words.

if we apply the exact same base MT system to use stemmed forms in alignment and/or translation, it is not a priori clear whether we would get a better result than if we apply the system to use fully inflected forms.


For this and other methods, if we are working with an m-best list from the treelet system, every translation hypothesis contains the annotations A that our model needs, because the system maintains the alignment, parse trees, etc., as part of its search space.

we do not need to do anything further to obtain input of the form necessary for application of the inflection model.


Since computers have limited means of human-like perception, the latter approach is also very much suited to the automatic acquisition of world knowledge by a computer (see Hearst, 1992;Charniak and Berland, 1999;Etzioni et al., 2004;Völker et al., 2005;Almuhareb and Poesio, 2005;Cimiano and Wenderoth, 2007;Veale and Hao, 2007).

by using the web as a distributed text corpus (see Keller et al., 2002), a multitude of facts and beliefs can be extracted, for purposes ranging from questionanswering to ontology population.


Moreover, EM optimizes a marginal likelihood score that is not discriminative.

most previous work that has attempted semi-supervised or unsupervised approaches to parsing have not produced results beyond the state of the art supervised results (Klein and Manning, 2002;.


As we will demonstrate below, this approach admits an efficient training procedure that can find a global minimum, and, perhaps surprisingly, can systematically improve the accuracy of supervised training approaches for learning dependency parsers.

in this paper, we focus on semi-supervised language learning, where we can make use of both labeled and unlabeled data.


The resulting loss function has a hat shape (usually called hat-loss), which is non-convex.

the objective as a whole is non-convex, making the search for global optimal difficult.


As mentioned in Section 3, a dependency tree Y j is represented as an adjacency matrix.

we need to enforce some constraints in the adjacency matrix to make sure that each Y j satisfies the dependency tree constraints.


However, in some cases, the correct translation may have lower probability.

more studies are needed on combination with other techniques as supplements.


Syllable expansion based on syllable similarity: If two syllables have similar alignment probability with every pinyin, we can view these two syllables as synonymy.

if a syllable is in the query, its synonymies should be contained too.


Many of them are redundant, which make decoding very slow.

effective rule optimization and pruning algorithms are highly desirable.


The NB tag scheme simply labels every letter as either being at a syllable boundary (B), or not (N).

the word im-mor-al-ly is tagged N B N N B N B N N , indicating a syllable boundary after each B tag.


Silent letters are problematic, and some letters can behave differently depending on their context (in English, consonants such as m, y, and l can act as vowels in certain situations).

it is difficult to generate ONC tags for orthographic forms without at least a cursory knowledge of the language and its principles.


Similarly, a fourgram like tion very often forms a syllable in and of itself.

in addition to the single-letter features outlined above, we also include in our representation any bigrams, trigrams, four-grams, and five-grams that fit inside our context window.


In (Galley et al., 2004), rules contain NT slots and combination is only allowed at those slots.


It is easy to verify the following theorem according to the definitions.

we can always raise a fixed structure if we assume it is complete, i.e.


Plus, except for n-best reranking, most discriminative methods require repeated parsing of the training set, which is generally impratical (Petrov and Klein, 2008).

previous work often resorts to extremely short sentences (≤ 15 words) or only looked at local features (Taskar et al., 2004;Henderson, 2004;Turian and Melamed, 2007).


For occurrences of links x1 , x2 , the element of π at a row addressed by x 1 and a column addressed by x 2 is 1 if x 1 = x 2 , and 0 otherwise.

each row and column of π has exactly two non-zero entries.


Since β is fully contained within α, this node is below the root of α but not below its gap.

the removal process leaves α with the links (links(α)−links(β))∪{ x }, where x is the link added in the removal process; the removal is validity preserving.


While POS taggers such as TreeTagger are common, and there some supertaggers are available, notably that of Clark and Curran (2007) for CCG, no standard supertagger exists for HPSG.

we developed a Maximum Entropy model for supertagging using the OpenNLP implementation.


One difference between the POS and lexical type taggers is that the POS tagger could produce multiple tags per word.

for the next experiment, we altered the lexical type tagger so it could also produce multiple tags.


We used three out of the five physics problems from the original real corpus to ensure the variety of dialog contents while keeping the corpus size small.

the evaluation corpus consisted of 180 dialogs, in which 15 dialogs were generated by each of the 4 student models on each of the 3 problems.


Note that we randomly picked two judges to rate each dialog so that different dialogs are rated by different pairs of judges and one pair of judges only worked on one dialog together.

the kappa agreements here do not reflect the agreement of one pair of judges.


Also, these measures are designed for comparison between models instead of prediction.

in Section 6.2, we build a ranking model to utilize the measures in their comparative manner.


While LOSS indicates how many pairs of dialogs are ranked correctly, our main focus here is to rank the performance of the four student models instead of individual dialogs.

we propose another Averaged Model Ranking (AMR) score.


When applying AMR on the minus-one-model cross validation results, we see similar results that the ranking model reproduces hu-  Table 8: AMR Scores for Regular Cross Validation man judges' rankings.

we suggest that the ranking model can be used to evaluate a new simulation model by ranking it against several old models.


However, in the context of spoken dialog system for domain-specific tasks, this framework must solve two problems: (1) Keeping track of the dialog state with a view to ensuring steady progress towards task completion, (2) Supporting n-best recognition hypotheses to improve the robustness of dialog manager.

we sought to solve these prob- lems by integrating the agenda graph as a heuristic which reflects the natural hierarchy and order of subtasks needed to complete the task.


In the agenda graph G, each node v should hold relevant dialog examples corresponding to user goal states.

the dialog examples in DEDB are Figure 4: XML description for the agenda graph mapped to a user goal state when a precondition of the node is true.


NEW TASK, which is placed on the node linked to root node, involves starting a new task to complete a new agenda.

a dialog is re-started and the current node is pushed onto the focus stack with the current user goal as its first element.


In the agenda graph G, each transition has its own probability as prior knowledge.

when c i is NEXT TASK, the discourse score is computed as where P (c i |c = top(S)) is a transition probability from the top node c on the focus stack S to the candidate node c i .


One of the fundamental characteristics of HCI is an error prone communication channel.

the simulation of channel noise is an important aspect of the learning environment.


We assume that the BOW corpus is generated from a bigram LM θ in two steps: (i) An ordered document is generated from the bigram LM θ; (ii) The document's unigram counts are collected to produce the BOW x.

the probability of a BOW x being generated by θ can be computed by marginalizing over unique orderings z of x: where σ(x) is the set of unique orderings, and |x| is the document length.


We generally assume that the potential function is a non-negative real value function.

the exponentiated weighted sum over the features of a clique is widely used, so that, is a feature vector obtained from the corresponding clique c in G(x, y).


That is, p j (x j , y; θ j )= c p j (x jc , y c ; θ j ).

we can incorporate generative models such as Bayesian networks including (1D and 2D) hidden Markov models (HMMs) as these joint PMs.


Since each p j (x jc , y c ) has range [0, 1], which is nonnegative, Ψ c can also be used as a potential function.

the conditional model for our SSL can be written as: where Z (x) = y∈Y c∈C Ψ c (y c , x; λ , Θ).


With a fixed Θ, the log-likelihood, log p j , can be seen simply as the feature functions of JESS-CM as with f i .

embedded joint PMs do not violate the global convergence conditions.


As a result, as with supervised CRFs, it is guaranteed that λ has a value that achieves the global maximum of L 1 (λ |Θ).

moreover, we can obtain the same form of gradient as that of supervised CRFs (Sha and Pereira, 2003), that is, we can easily optimize L 1 by using the forward-backward algorithm since this paper solely focuses on a sequence model and a gradient-based optimization algorithm in the same manner as those used in supervised CRF parameter estimation.


MDF estimation substitutes p(x, y) with discriminant functions g(x, y).

to estimate the parameter Θ of JESS-CM by using MDF estimation, the following objective function is maximized with a fixed λ : where p(Θ) is a prior probability distribution of Θ.


This means that the calculation cost for unlabeled data is dominant.

in order to make the overall parameter estimation procedure Initialize: update one step toward maximizing L 2 (Θ|λ ) with fixed λ using Du.


In addition, the calculation cost for estimating parameters of embedded joint PMs (HMMs) is independent of the number of HMMs, J, that we used (Suzuki et al., 2007).

the cost for calculating the JESS-CM parameters, λ and Θ, is essentially the same as executing T iterations of the MML estimation for a single HMM using the EM algorithm plus T + 1 time optimizations of the MAP estimation for a conventional supervised CRF if it converged when t = T .


We can easily ignore overlapping features that appear in a single HMM.

47, 39 and 79 distinct HMMs are embedded in the potential functions of JESS-CM for POS tagging, chunking and NER experiments, respectively.


However, it may be computationally unrealistic to retrain the entire procedure several times using 1G-words of unlabeled data.

these tunable parameter values are selected using a relatively small amount of unlabeled data (17M-words), and we used the selected values in all our experiments.


That is, Θ traverses two or more local maxima.

we examined its convergence property experimentally.


For example, if one wanted to extract a list of company acquisitions, " ACQUIRER acquired ACQUIREE " would be one surface pattern with " ACQUIRER " and " ACQUIREE " as the slots to be extracted.

we can claim: Claim 2: These paraphrases can then be used for generating high precision surface patterns for relation extraction.


Similarly, the rule 'X charge Y → X accuse Y ' should not be applied to "This store charged my account", since the assumed sense of 'charge' in the rule is different than its sense in the text.

the intended contexts for h and r and the context within the given t should be properly matched to verify valid inference.


h: 'X invented the PC' for "Who invented the PC?").

cp g (h) can be constructed with respect to the question's focus while cp v (h) may be generated from the expected answer type (Moldovan et al., 2000;Harabagiu et al., 2003).


For comparison with prior work, we follow (Pantel et al., 2007) and represent preferences for vari-able instantiations using a distributional approach, and in addition incorporate a standard specification of named-entity types.


For Injure, the set of hypotheses included 'A injure V' and 'injure V in T' where role(A)={Agent, In-strument}, role(V)={Victim}, and role(T)={Time, Place}.

correct match of an argument corresponds to correct role identification.


They aim to find relationship instances rather than identify generic semantic relationships.

their representation is very different from ours.


It is also possible to restrict selection of hook words to a specific domain or word type, if we want to discover only a desired subset of existing relationships.

we could sample hook words from nouns, verbs, proper names, or names of chemical compounds if we are only interested in discovering relationships between these.


Since content words determine the joining of patterns into clusters, the more ambiguous a word is, the noisier the resulting clusters.

higher values of F C allow more ambiguous words, increasing cluster recall but also increasing cluster noise, while lower ones increase cluster precision at the expense of recall.


They were also asked to subdivide the text of this answer into each of the following lengths: a word or phrase, a sentence, a paragraph, a section or an entire article.

2 the shorter answer passages are subsumed by the longer ones.


In our problem, each node (sentence) might have multiple labels since one sentence could be the context of multiple questions in a thread.

it is difficult to find a solution to tag context sentences for all questions in a thread in single pass.


Using Skip-chain CRFs We observed in our corpus 74% questions lack constraints or background information which are very useful to link question and answers as discussed in Introduction.

contexts should be leveraged to detect answers.


The other 70% of the unknowns ( 5.3% of the words in the text in our experiments) will be assigned a wrong tag.

of this observation, our strategy is to focus on full morphological analysis for unknown tokens and apply a proper name classifier for unknown analyses and unknown tokens.


Our model utilizes a Dirichlet process prior for each language, as well as for the cross-lingual links (abstract morphemes).

a distribution over morphemes and morpheme alignments is first drawn from the set of Dirichlet processes and then produces the observed data.


(9), for a given word v only the set of classes which contain at least one word w for which N (v, w) > 0 must be considered, denoted by suc(v).

the second summation is equivalent to the further simplified criterion is: When exchanging a word w between two classes c and c , only two summands of the second summation of Eq.


Throughout one iteration of the algorithm, in which for each word in the vocabulary each possible move to another class is evaluated, this amounts to the number of distinct bigrams in the training corpus B, times the number of clusters N c .

the worst case complexity using the modified optimization criterion is in: Using this optimization criterion has two effects on the complexity of the algorithm.


Nominative case is used to define the nouns which are the subject of the sentence, accusative shows usually the direct object of the verbs and dative case refers to the indirect object of bitransitive verbs.

the followed approach takes advantage of syntax, following a method similar to Semantic Role Labelling (Carreras and Marquez, 2005;Surdeanu and Turmo, 2005).


As we parse the tree top-down, on every level, we look for two discrete nodes which, somewhere in their children, include the verb and the corresponding subject.

the node which contains the subject is searched recursively until a subject is found.


For the subject selection, the following rules were applied: • The verb person is directly connected to the subject of the sentence and in most cases it is directly inferred by a personal pronoun (I, you etc).

since this is usually the case, when a pronoun existed, it was directly used as a tag.


• The gender of the subject does not affect the inflection of the verb in Greek.

all three genders that are given by the third person pronouns were reduced to one.


The word "aspects" is found, which has a POS tag that shows it is a plural noun.

we consider the subject to be of the third person in plural (tagged by they) which is recursively passed to the children of the head node.


Although the source language misses morphology attributes required by the target language, the needed information is inherent in the syntactic structure of the source sentence.

we have shown that this information can be easily be included in a SMT model by preprocessing the source text.


Their method was based on the assumption that NEs are preserved across paraphrases.

the method acquired paraphrase patterns from sentence pairs that share comparable NEs.


The above approach assigns labels independently, without considering the whole predicate argument structure.

the classifier output may generate overlapping arguments.


It is worth noting that just observing the Arabic word 'president' in Fig 1, the system would assume that it is an indefinite word since it does not include the definite arti-cle .

the system could be lead astray to conclude that 'the-Chinese' does not modify 'president' but rather 'the-ministers'.


a degree larger than 2, the F 1 increases by more than 3 percentage points.

not only are the English features meaningful for Arabic but also their combinations are important, reveal-8 http://disi.unitn.it/∼moschitti 9 http://www.lsi.upc.es/∼srlconll/soft.html  ing that both languages share an underlying syntaxsemantics interface.


Further increasing the polynomial degree (Poly5-6) generates very complex hypotheses which result in very low accuracy values.

to improve the polynomial kernel, we sum it to the contribution of AST and/or EAST, obtaining AST+Poly3 (polynomial kernel of degree 3), EAST+Poly3 and AST+EAST+Poly3, whose F 1 scores are also shown in Figure 5.


By definition, the impact of a paper has to be judged based on the consent of research community, especially by people who cited it.

in order to generate an impact-based summary, we must use not only the original content, but also the descriptions of that paper provided in papers which cited it, making it a challenging task and different from a regular summarization setup such as news summarization.


We call such a window of sentences a citation context, and use C to denote the union of all the citation contexts of d in a collection of research papers.

c itself is a set (more precisely bag) of sentences.


However, unlike in the case of ad hoc retrieval, we do not really have a query describing the impact of the paper; instead, we have a lot of citation contexts that can be used to infer information about the query.

the main challenge in impact summarization is to effectively construct a "virtual impact query" based on the citation contexts.


We define pr(s) as pr(s) = 1 α k , where k is the distance (counted in terms of the number of sentences) between sentence s and the center sentence of the window containing s; by "center sentence", we mean the citing sentence containing the citation label.

the sentence with the citation label will have a proximity of 1 (because k = 0), while the sentences away from the citation label will have a decaying weight controlled by parameter α.


By design, our model is incapable of exploiting such phenomena, as our goal is to investigate the notion of gestural cohesion.

the performance gains demonstrated in this paper cannot be explained by such punctuation-like phenomena; we believe that they are due to the consistent gestural themes that characterize coherent topics.


* Both authors contributed equally to this work.

with an increasing coverage of a wide variety of domains in human language technology (HLT) systems, we can expect a growing need for manual annotations to support many kinds of application-specific training data.


Second, for dissimilar annotation tasks -take, e.g., a comprehensive HLT pipeline incorporating morphological, syntactic and semantic data -a classifier might require features as input which constitute the output of another preceding classifier.

training such a classifier which takes into account several annotation tasks will best be performed on a rich corpus annotated with respect to all inputrelevant tasks.


In a multiple annotation scenario, however, there are several annotation tasks to be accomplished at once and for each task typically a separate statistical model will then be trained.

the goal of multi-task AL is to query those examples for human annotation that are most informative for all learners involved.


For both WSJ and Brown only parse annotations though no entity annotations were available.

we enriched both corpora with entity annotations (three entities: person, location, and organization) by means of a tagger trained on the English data set of the CoNLL-2003 shared task (Tjong Kim Sang and De Meulder, 2003).


This is promising evidence that MTAL selection can be a better choice than one-sided selection in multiple annotation scenarios.

considering all annotation tasks in the selection process (even if the selection protocol is as simple as the alternating selection protocol) is better than selecting only with respect to one task.


KL-divergence is a natural measure of discrepancy between two distributions, but it is somewhat nontrivial to compute-for our three recursive models, it requires solving fixed point equations, and becomes completely intractable in face of label symmetry.

we propose a more manageable alternative: where we weight the difference between the j-th component of the parameter vectors by µ j , the jth expected sufficient statistic with respect to p θ (the expected counts computed in the E-step).


far, we have considered approximation and identifiability errors, which have to do with flaws of the model.


If we assume that the beam is not large enough for all the candidates at at each stage, then, from the newly generated candidates, the baseline POS tagger can keep 1/T for the next processing stage, while the joint model can keep only 1/2T , and has to discard the rest.

even when the candidate comparison standard is ignored, we can still see that the chance for the overall best candidate to fall out of the beam is largely increased.


Open features, such as knowledge of numbers and European letters, and relationships from semantic networks (Shi and Wang, 2007), have been reported to improve the accuracy of segmentation and POS tagging.

given the flexibility of the feature-based linear model, an obvious next step is the study of open features in the joint segmentor and POS tagger.


However, Kominek and Black (2006) show that in languages with a less transparent relationship between spelling and pronunciation, such as English, Dutch, or German, the number of letter-to-sound rules grows almost linearly with the lexicon size.

most recent work in this area has focused on machine-learning approaches.


Given a set of related evidences, we assume that the generation of an expert candidate c is independent with topic t and omit it in expert matching.

we simplify the expert matching formula as below: where P(c|p) depends on how an expert candidate c matches to a person occurrence p (e.g.


Every sequence of words in a book represents a potential candidate for an entry in the back-of-the-book index.

we extract from the training and the test data sets all the n-grams (up to the length of four), not crossing sentence boundaries.


We assume that all mentions in an email share the same contextual space.

we can treat the context of a mention as the context of its email.


For the guided graph-based model, which we call MST Malt , this feature representation is modified to include an additional argument G Malt x , which is the dependency graph predicted by MaltParser on the input sentence x.

the new feature representation will map an arc and the entire predicted Malt-Parser graph to a high dimensional feature representation, f (i, j, l, G Malt x ) ∈ R k+m .


In spite of this problem, many of the dependency parsers described in the literature are constructive, in the sense that they proceed by combining smaller structures to form larger ones until they find a complete parse for the input sentence.

it is possible to define a variant of parsing schemata, where these structures can be defined as items and the strategies used for combining them can be expressed as inference rules.


Such spans cannot be represented by a single dependency tree.

our formalism allows items to be sets of forests of partial dependency trees, instead of sets of trees.


Among approaches to multilingual grammar engineering, the Grammar Matrix's distinguishing characteristics include the deployment of a shared core grammar for crosslinguistically consistent constraints and a series of libraries modeling varying linguistic properties.

while other work has successfully exploited grammar porting between typologically related languages (e.g., Kim et al., 2003), to my knowledge, no other grammar porting project has covered the same typological dis-tance attempted here.


Unfortunately, as was shown by Fraser and Marcu (2007) AER can have weak correlation with translation performance as measured by BLEU score (Papineni et al., 2002), when the alignments are used to train a phrase-based translation system.

in addition to AER, we focus on precision and recall.


Formally, the approach we take can be thought of as a "noisier channel", where an observed signal o gives rise to a set of source-language strings f ∈ F Following Och and Ney (2002), we use the maximum entropy framework (Berger et al., 1996) to directly model the posterior P r(e, f |o) with parameters tuned to minimize a loss function representing the quality only of the resulting translations.

we make use of the following general decision rule: In principle, one could decode according to (2) simply by enumerating and decoding each f ∈ F(o); however, for any interestingly large F(o) this will be impractical.


Arabic orthography is problematic for lexical and phrase-based MT approaches since a large class of functional elements (prepositions, pronouns, tense markers, conjunctions, definiteness markers) are attached to their host stems.

while the training data may provide good evidence for the translation of a particular stem by itself, the same stem may not be attested when attached to a particular conjunction.


We find that most paraphrases extracted using the method of Barzilay and McKeown (2001) are quite short.

we employ a new approach for paraphrase extraction.


First, the largest PT used in the experiments is extracted using the bilingual parallel data, which are mostly from news documents.

the test set of news sentences is more similar to the training data.


We contacted the LCC authors to obtain their datasets, but they were unable to make them available to us.

we simulated the LCC negation corpus, adding negative markers to the RTE2 test data (Neg test), and to a development set (Neg dev) constructed by randomly sampling 50 pairs of entailments and 50 pairs of non-entailments from the RTE2 development set.


Contradiction features are extracted based on mismatches between the text and hypothesis.

we must first remove pairs of sentences which do not describe the same event, and thus cannot be contradictory to one another.


We wanted the system to be as language-independent as possible, so we refrained from using any taggers or parsing tools.

instances are extracted using only word boundaries and orthographic information.


An ME model estimates the conditional probability of the events given the corresponding features.

all the information must be expressed in terms of features in a pre-processing step.


TF*IDF) information in calculating the similarity between sentences.

our mixture model for answering complex questions is: Where TREESIM(s,q) is the normalized syntactic (and/or semantic) similarity between the query (q) and the document sentence (s) and C is the set of all sentences in the collection.


LSA has been shown to be effective for NLP based problems but has yet to be applied for dialect classification.

we develop an approach that uses a combination with ngram language modeling and LSA processing to achieve effective language based dialect classification accuracy.


We also note that there is no speaker overlap between the test and train data.

there are no additional acoustic clues other than dialect.


An automatic VSD system usually has at its disposal a diverse set of features among which the semantic features play an important role: verb sense distinctions often depend on the distinctions in the semantics of the target verb's arguments (Hanks, 1996).

some method of capturing the semantic knowledge about the verb's arguments is crucial to the success of a VSD system.


However, unlike Czech, case frequently does not convey distinct meaning in Icelandic as it is often determined by elements such as the governing verb in a clause (non-local information).

while Icelandic case looks formally like Slavic and presents similar challenges for POS tagging, it also may be syntactically-determined, as in Standard Arabic.


Learning systems can be easily applied to new corpora, tagsets, or languages and can accommodate integration of other systems (including rule based) or new linguistic resources, such as those used by Loftsson.

we seek a learning system that can handle these challenges.


It is in this situation that the tagger makes most case-assignment errors.

while accuracy depends on correct case assignment for these nouns, other tags are mostly unaffected.


We find that models trained on younger users, but tested on older users (Y-O) perform worse than models trained on older users / all users and tested on older users (O-O, A-O).

models of the behaviour of younger users cannot be used to simulate older users.


They are also in line with findings of tests of deployed Interactive Voice Response systems with younger and older users (Dulude, 2002), which show the diversity of older people's behaviour.

we have shown that standard metrics for evaluating SUs are a good predictor of the behaviour of our two user types.


We work with a standard Arabic-English SMT system that has been already optimized for minimizing data sparsity through the use of morphological preprocessing and orthographic normalization.

our baseline token OOV rate is rather low (average 2.89%).


These results were achieved without the use of any NLP features.

the developed system is adjustable to any specific type of texts, simply by changing the database of texts and abbreviations.


If annotators are required to change only those automatically generated tags that are incorrect, and depending on how annotators are paid, the cost of tagging one sentence can depend greatly on what is known from sentences already annotated.

in POS tagging both the benefit (increase in accuracy) and cost of annotating a sentence depend not only on properties of the sentence but also on the order in which the items are annotated.


Thus, in POS tagging both the benefit (increase in accuracy) and cost of annotating a sentence depend not only on properties of the sentence but also on the order in which the items are annotated.

when evaluating the performance of an AL technique, cost should be taken into account.


In such a curve, the accuracy is the independent variable.

we then compute the percent reduction in cost (e.g., number of words or hours) over the cost of the random baseline for the same accuracy a: the random baseline represents the trajectory redux(a) = 0.0.


For example, in an article on a sightseeing trip, which should be RECREATION, the author frequently mentions the means of transportation.

the article was wrongly categorized as TRAFFIC.


ASR hypotheses which result in the same user input are merged (summing their probabilities), and the resulting list of at most three ASR-SLU hypotheses are passed to the dialogue manager.

the number of MDP states in the dialogue manager grows by up to three times at each step, before pruning.


From the bilingual sentence pairs, which contain number tags, 66.52% do not contain the same number of tags on the Chinese and the English side.

52% of the phrase pairs in the phrase table, which contain number tags had to be removed, because the tags were unbalanced.


• Category Features: We hypothesized that user behavior (and asker satisfaction) varies by topical question category, as recently shown in reference ).

we model the prior of asker satisfaction for the category, such as the average asker rating (satisfaction).


NANC is a news corpus, quite like WSJ data.

the point of that paper was that self-training a WSJ parser on similar data makes the parser more flexible, not better adapted to the target domain in particular.


It said nothing about the task we address here.

our claim is that previous results are quite ambiguous on the issue of bridging corpora for parser adaptation.


Genia is a corpus of abstracts from the Medline database selected from a search with the keywords Human, Blood Cells, and Transcription Factors.

the Genia treebank data are all from a small domain within Biology.


After all, they are written to introduce someone ignorant of a field to the ideas and terminology within it.

one might expect that the English of a Biology textbook would be intermediate between the more typical English of a news article and the specialized English native to the domain.